---
title: "COVID-19"
output: 
  flexdashboard::flex_dashboard:
    logo: rbmv_curve.png 
    orientation: rows
    source_code: embed
    theme: flatly
---

 

```{r setup, include=FALSE}
library(flexdashboard)
```

```{r, include=FALSE}
# Packages
library(tidyverse)
library(lubridate)
library(rvest)
library(sf)
library(leaflet)
library(plotly)
library(crosstalk)
library(rbmv)
library(classInt)

# imported functions
`%<>%` <- magrittr::`%<>%`

# custom functions
get_bins <- function(.data, bins = 8) {
  #' use kmeans to automatically set sensible bins that represent the data well
  #' requires 'classInt' package
  #' @return a vector with 8 default bins 
  intervals <- classIntervals(.data, n = bins, style = "kmeans", rtimes = 5)
  return(round(intervals$brks))
}

# API key
mapbox <- read_lines(".mapbox-key")

# Get the data + tidy
dph_covid19_page <- read_html("http://www.publichealth.lacounty.gov/media/Coronavirus/locations.htm")

dph_covid19_tbls <- dph_covid19_page %>%
  html_nodes("table") %>%
  html_table()

dph_covid19_messy <- dph_covid19_tbls[[1]] %>%
  repair_names() %>%
  janitor::clean_names() %>%
  rename(locations = x1, total_cases = x2) %>%
  select(-x3)

dph_covid19_totals <- dph_covid19_messy %>%
  slice(2:11)
  
lac_total_cases <- parse_number(dph_covid19_totals$total_cases[[5]])
lac_total_deaths <- parse_number(dph_covid19_totals$total_cases[[10]])

dph_covid19_totals %>%
  mutate(locations = str_remove_all(locations, "- ")) %>%
  filter(locations %in% c("Long Beach", "Pasadena")) %>%
  slice(1:2) %>%
  mutate(locations = str_glue("City of {locations}") %>% parse_character(),
         total_cases = parse_number(total_cases)) -> lb_pass

dph_covid19 <- dph_covid19_messy %>% slice(53:391) %>%
  mutate(
    locations = str_remove_all(locations, regex("\\*")),
    total_cases = parse_number(total_cases, na = "--"),
    locations = case_when(
      str_detect(locations, "San Francisquito") ~ "Unincorporated - San Francisquito Canyon/Bouquet Canyon",
      TRUE ~ locations)) %>%
  filter(!locations == "Los Angeles") %>%
  bind_rows(., lb_pass)

captions <- dph_covid19_page %>% 
  html_nodes("caption") %>%
  html_text()

str_remove_all(captions[1], "\\r|\\n|\\t") %>%
  str_split_fixed(., "\\*", 2) -> page_updated

# page_updated <- str_split_fixed(page_updated[1], " ", 4)[4]
# page_updated <- str_glue("{page_updated}/20")
# page_updated <- parse_date(page_updated, "%m/%d/%y")

# uncomment line below if the 'caption' html is up to date on the DPH website
# not sure if data is only updated the night before or not; in which case I will just update based 
# on the day new data is available
page_updated <- today()

page_updated_string <- str_glue("{wday(page_updated, label = T)} {month(page_updated, label = T)} {day(page_updated)}, {year(page_updated)}")

gis <- st_read(
  dsn = "data/gis/geo_export_871186bb-3266-4a1c-a0f6-8cda86b55d55.shp",
  layer = "geo_export_871186bb-3266-4a1c-a0f6-8cda86b55d55",
  quiet = T
)

gis %<>% st_transform(crs = 4326) %>%
  mutate(label = as.character.factor(label))

lac_covid19 <- gis %>% 
  left_join(., dph_covid19, by = c("label" = "locations")) %>%
  mutate(total_cases = if_else(is.na(total_cases), 0, total_cases))

covid19_cases <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv")

covid19_deaths <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv")

make_link <- function(date) {
  date_format <- format(date, format = "%m-%d-%Y")
  link <- str_glue("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{date_format}.csv")
  return(link)
}

test_data <- function(date) {
  link <- make_link(date)
  status <- httr::http_status(httr::GET(link))
  return(status$category)
}

get_world_data <- function(date) {
  if (test_data(date) == "Success") {
    return(make_link(date))
  } else if (test_data(date) == "Client error") {
    return(make_link(date - 1))
  } 
}

link <- get_world_data(today())

world <- read_csv(link) 
world %<>% rename(long = Long_, lat = Lat)

covid19_time_series_cases <- covid19_cases %>% 
  filter(Admin2 == "Los Angeles") %>%
  gather("date", "cases", 12:ncol(covid19_cases)) %>%
  select(Admin2, date, cases) %>%
  rename(county = Admin2) %>%
  mutate(date = parse_date(date, "%m/%d/%y"),
         case_type = "New Confirmed Case") %>%
  bind_rows(
    tribble(
      ~county, ~date, ~cases, ~case_type, 
      "Los Angeles", page_updated, lac_total_cases, "New Confirmed Case")) %>%
  distinct()

covid19_time_series_deaths <- covid19_deaths %>% 
  filter(Admin2 == "Los Angeles") %>%
  gather("date", "cases", 13:ncol(covid19_deaths)) %>%
  select(Admin2, date, cases) %>%
  rename(county = Admin2) %>%
  mutate(date = parse_date(date, "%m/%d/%y"),
         case_type = "Death") %>%
  bind_rows(
    tribble(
      ~county, ~date, ~cases, ~case_type, 
      "Los Angeles", page_updated, lac_total_deaths, "Death")) %>%
  distinct()

covid19_time_series <- bind_rows(covid19_time_series_cases, covid19_time_series_deaths) 

covid19_cases %<>% mutate(Combined_Key = str_remove(Combined_Key, ", US"))

covid19_cases_top10_us <- covid19_cases %>%
  arrange(desc(.[[ncol(covid19_cases)]])) %>%
  slice(1:10) %>%
  gather("date", "cases", 12:ncol(covid19_cases)) %>%
  select(Combined_Key, date, cases) %>%
  rename(location = Combined_Key) %>%
  mutate(
    location = str_remove(location, ", US"),
    date = parse_date(date, "%m/%d/%y"),
    case_type = "New Confirmed Case") %>%
  distinct()

covid19_state_cases <- covid19_cases %>% 
  filter(iso3 == "USA") %>%
  gather("date", "cases", 12:ncol(covid19_cases)) %>%
  rename(state = Province_State) %>%
  group_by(state, date) %>%
  summarise(cases = sum(cases)) %>%
  mutate(date = parse_date(date, "%m/%d/%y"))

light_blue <- rbmv_pal("main", plotly = T)[1]
ruby <- rbmv_pal("main", plotly = T)[2]
pale_black <- rbmv_pal("main", plotly = T)[5]

sd <- SharedData$new(covid19_cases_top10_us, ~location, "Select a city")
```

LA County {data-icon="fa-map"}
=====================================

Row {data-height=510}
-------------------------------------

###

```{r}
bins <- get_bins(lac_covid19$total_cases, bins = 12) 

pal <- colorBin(
  palette = rbmv_pal("spectrum", plotly = T), 
  domain = lac_covid19$total_cases, 
  bins = bins)

label <- str_glue("{lac_covid19$label}

               Total cases: {lac_covid19$total_cases}") %>%
  lapply(htmltools::HTML)

lac_covid19 %>%
  leaflet() %>%
  addTiles(mapbox, attribution = paste("Data from Los Angeles County Department of Public Health COVID-19 location table")) %>%
  addPolygons(
    fillColor = ~pal(total_cases),
    weight = 1.5,
    fillOpacity = 0.7,
    smoothFactor = 0.5,
    color = "white",
    label = label,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    pal = pal, values = ~total_cases, opacity = 0.7,
    title = NULL, position = "bottomright") %>%
  setView(-118.2, 34, zoom = 9.5)
```

Row {data-height=400}
-------------------------------------

###

```{r}
source <- list(
  x = 1, y = -0.25, text = "Source: 2019 Novel Coronavirus COVID-19 (2019-nCoV)\nData Repository by Johns Hopkins CSSE",
  showarrow = F, xref = "paper", yref = "paper", xanchor = "right", yanchor = "auto",
  xshif = 0, yshift = 0, font = list(size = 12, color = "grey")
)

base <- plot_ly(sd, color = I(rbmv_pal(plotly = T)[6]), height = 320) %>%
  group_by(location)

left_bar <- base %>%
  add_bars(x = ~cases, y = ~location, hoverinfo = "text",
           text = ~paste("Click bar to highlight line
on the left")) %>%
  layout(
    barmode = "overlay",
    xaxis = list(title = ""),
    yaxis = list(
      title = "",
      categoryorder = "max ascending")
  )

right_line <- base %>%
  add_lines(x = ~date, y = ~cases, alpha = 0.3, hoverinfo = "text",
          text = ~paste("
 Location: ", location,
                        "
 Case type:  ", case_type, "x Log Scale",
                        "
 Cases: ", scales::comma(cases))) %>%
  layout(xaxis = list(title = ""),
         yaxis = list(type = "log"))

subplot(left_bar, right_line, titleX = TRUE, widths = c(.3, .7)) %>%
  layout(title = "How does Los Angeles County compare to other major outbreak centers", 
         margin = list(l = 120, b = 40), annotations = source) %>%
  hide_legend() %>%
  highlight("plotly_click", color = rbmv_pal(plotly = T)[2]) %>%
  config(displayModeBar = FALSE)
```

Row {data-height=110}
-------------------------------------

###

```{r}
valueBox(page_updated_string, color = light_blue, icon = "fa-calendar", caption = "Data pulled from both LAC DPH and John Hopkins CSSE")
```


### 

```{r}
total_cases <- scales::comma(last(covid19_time_series_cases$cases))
valueBox(total_cases, color = ruby, icon = "fa-ambulance", caption = "Total Lab Confirmed COVID-19 Cases in LAC")
```

### 

```{r}
total_deaths <- scales::comma(last(covid19_time_series_deaths$cases))
valueBox(total_deaths, color = pale_black, icon = "fa-medkit", caption = "Total COVID-19 Related Deaths")
```

US States {data-icon="fa-chart-line"}
=====================================

###

```{r}
bins <- get_bins(covid19_state_cases$cases, bins = 9)
colors <- rbmv_pal("spectrum", plotly = TRUE)

normalize <- function(x) round((x-min(x))/(max(x)-min(x)), 4)

state_colors <- tibble(range = bins, hex = colors) %>%
  mutate(range = normalize(range))

plot_ly(covid19_state_cases) %>%
  add_heatmap(x = ~date, y = ~state, z = ~cases,
              colorscale = state_colors,
              showscale = F, opacity = .85,
              text = ~paste(
                "Date: ", date, "
State: ", state,
                "
Total confirmed cases: ", scales::comma(cases)),
              hoverinfo = "text") %>%
  layout(
    title = "COVID19 Cases",
    xaxis = list(title = "Date"),
    yaxis = list(title = "", autorange = "reversed")) %>%
  config(displayModeBar = FALSE)
```


Global {data-icon="fa-globe"}
=====================================

###

```{r}
bins <- get_bins(world$Confirmed, bins = 12) 

pal <- colorBin(
  palette = rbmv_pal("warm", plotly = T), 
  domain = world$Confirmed, 
  bins = bins)

label <- str_glue("Location: {world$Combined_Key}
      
Cases: {scales::comma(world$Confirmed)}
      
Deaths: {scales::comma(world$Deaths)}
      
Active: {scales::comma(world$Active)}
      
Recovered: {scales::comma(world$Recovered)}
      
Last Update: {world$Last_Update}") %>%
  lapply(htmltools::HTML)

world %>%
  leaflet() %>%
  addTiles(mapbox, attribution = paste("Data from Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE")) %>%
  addCircleMarkers(
    lng = ~long, lat = ~lat,
    label = ~label,
    color = ~pal(Confirmed),
    radius = ~ifelse(Confirmed >= 30000, 25, 15),
    stroke = FALSE, fillOpacity = .7,
    clusterOptions = markerClusterOptions(
      showCoverageOnHover = FALSE),
  labelOptions = labelOptions(
    style = list("font-weight" = "normal", padding = "3px 8px"),
    textsize = "15px",
    direction = "auto")) %>%
  addLegend(title = "COVID-19 Cases", opacity = 0.6, pal = pal, values = ~Confirmed, position = "bottomright")
```